AI training data
Eight years building training data, human data operations and evaluation programmes for the labs and platforms defining frontier AI — and for enterprises that need their own data to be an advantage rather than a liability.
Decide what data you actually need before you buy any of it.
Model goals translated into a data plan: domains, languages, modalities, volumes and the order to build them in — so spend follows capability, not vendor appetite.
Data you can defend in a room full of lawyers.
Provenance, consent, rights and territory handled up front, with vendor selection and commercial structures that hold up as models and markets change.
Expert humans, at scale, without quality collapse.
Annotation and expert-contributor programmes designed end to end: recruiting, guidelines, calibration, pay and throughput, run across time zones and languages.
Preference data that moves the model in the direction you meant.
Instruction, preference and reasoning data programmes with rubric design, inter-rater reliability and the feedback loops that keep signal clean as models improve.
Know whether the model got better, not just different.
Task suites, human evaluation harnesses and domain benchmarks tied to the decisions the model is trusted with — plus the reporting leadership can act on.
Find the failures before your users do.
Adversarial and safety data programmes for models and agents: taxonomies, attack coverage, escalation paths and the evidence trail your risk function will ask for.
Teach agents how to reason, act and recover.
Data for systems that do more than generate an answer — they plan, use tools, take actions and pursue goals across workflows. Trajectory design, tool-use data, multi-step task data, agent-to-agent interactions, human escalation, failure recovery and long-horizon evaluation.
Create the data the real world hasn’t given you yet.
Generate targeted data for sparse, sensitive, dangerous or hard-to-capture scenarios — designed for coverage, not simply volume. Synthetic training and evaluation data, personas, scenario generation, edge cases, rare events and simulated environments for models and agents.
Every safety claim, every evaluation, every agent you let act on its own traces back to the data it learned from and the humans who shaped it. Get that layer right and the rest of the AI programme becomes defensible.